Overview

Dataset statistics

Number of variables27
Number of observations3000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory632.9 KiB
Average record size in memory216.0 B

Variable types

Numeric8
Categorical15
Boolean1
DateTime3

Alerts

PatientID has unique valuesUnique
NumHealthcareVisits has 164 (5.5%) zerosZeros

Reproduction

Analysis started2024-07-16 01:15:51.019612
Analysis finished2024-07-16 01:16:17.617631
Duration26.6 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

PatientID
Real number (ℝ)

UNIQUE 

Distinct3000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9031143 × 1012
Minimum1.2776501 × 108
Maximum9.9959456 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2024-07-16T01:16:17.852488image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1.2776501 × 108
5-th percentile5.2104461 × 1011
Q12.4519326 × 1012
median4.829809 × 1012
Q37.3470709 × 1012
95-th percentile9.4439364 × 1012
Maximum9.9959456 × 1012
Range9.9958178 × 1012
Interquartile range (IQR)4.8951383 × 1012

Descriptive statistics

Standard deviation2.859756 × 1012
Coefficient of variation (CV)0.58325297
Kurtosis-1.1846337
Mean4.9031143 × 1012
Median Absolute Deviation (MAD)2.4522073 × 1012
Skewness0.048171061
Sum1.4709343 × 1016
Variance8.1782043 × 1024
MonotonicityNot monotonic
2024-07-16T01:16:18.267964image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.479647884 × 10121
 
< 0.1%
6.554672046 × 10121
 
< 0.1%
5.967086123 × 10111
 
< 0.1%
5.79265433 × 10121
 
< 0.1%
1.033866484 × 10121
 
< 0.1%
6.50918191 × 10121
 
< 0.1%
2.518596627 × 10121
 
< 0.1%
3.952356797 × 10121
 
< 0.1%
6.67795844 × 10121
 
< 0.1%
9.530799161 × 10121
 
< 0.1%
Other values (2990) 2990
99.7%
ValueCountFrequency (%)
127765011 1
< 0.1%
2701347156 1
< 0.1%
3485322605 1
< 0.1%
2.166624908 × 10101
< 0.1%
2.57588564 × 10101
< 0.1%
2.612047544 × 10101
< 0.1%
2.838905707 × 10101
< 0.1%
2.936088486 × 10101
< 0.1%
4.52462392 × 10101
< 0.1%
4.854021345 × 10101
< 0.1%
ValueCountFrequency (%)
9.995945605 × 10121
< 0.1%
9.984733852 × 10121
< 0.1%
9.983869172 × 10121
< 0.1%
9.983855358 × 10121
< 0.1%
9.982106498 × 10121
< 0.1%
9.981010584 × 10121
< 0.1%
9.979627801 × 10121
< 0.1%
9.977829731 × 10121
< 0.1%
9.973363977 × 10121
< 0.1%
9.970791953 × 10121
< 0.1%

Age
Real number (ℝ)

Distinct62
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.245667
Minimum18
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2024-07-16T01:16:18.626942image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile21
Q133
median48
Q364
95-th percentile77
Maximum79
Range61
Interquartile range (IQR)31

Descriptive statistics

Standard deviation17.867038
Coefficient of variation (CV)0.37033457
Kurtosis-1.1937588
Mean48.245667
Median Absolute Deviation (MAD)15
Skewness0.030996074
Sum144737
Variance319.23106
MonotonicityNot monotonic
2024-07-16T01:16:19.017059image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38 70
 
2.3%
77 65
 
2.2%
57 62
 
2.1%
69 59
 
2.0%
42 58
 
1.9%
34 57
 
1.9%
33 57
 
1.9%
50 56
 
1.9%
52 56
 
1.9%
27 56
 
1.9%
Other values (52) 2404
80.1%
ValueCountFrequency (%)
18 42
1.4%
19 54
1.8%
20 50
1.7%
21 50
1.7%
22 54
1.8%
23 50
1.7%
24 45
1.5%
25 42
1.4%
26 40
1.3%
27 56
1.9%
ValueCountFrequency (%)
79 47
1.6%
78 47
1.6%
77 65
2.2%
76 30
1.0%
75 54
1.8%
74 48
1.6%
73 46
1.5%
72 45
1.5%
71 51
1.7%
70 36
1.2%

Gender
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.6 KiB
Male
1544 
Female
1456 

Length

Max length6
Median length4
Mean length4.9706667
Min length4

Characters and Unicode

Total characters14912
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowFemale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Male 1544
51.5%
Female 1456
48.5%

Length

2024-07-16T01:16:19.418520image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-16T01:16:19.692316image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
male 1544
51.5%
female 1456
48.5%

Most occurring characters

ValueCountFrequency (%)
e 4456
29.9%
a 3000
20.1%
l 3000
20.1%
M 1544
 
10.4%
F 1456
 
9.8%
m 1456
 
9.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 11912
79.9%
Uppercase Letter 3000
 
20.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 4456
37.4%
a 3000
25.2%
l 3000
25.2%
m 1456
 
12.2%
Uppercase Letter
ValueCountFrequency (%)
M 1544
51.5%
F 1456
48.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 14912
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 4456
29.9%
a 3000
20.1%
l 3000
20.1%
M 1544
 
10.4%
F 1456
 
9.8%
m 1456
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14912
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 4456
29.9%
a 3000
20.1%
l 3000
20.1%
M 1544
 
10.4%
F 1456
 
9.8%
m 1456
 
9.8%

Race
Categorical

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size23.6 KiB
Hispanic
612 
Other
609 
Asian
598 
White
598 
Black
583 

Length

Max length8
Median length5
Mean length5.612
Min length5

Characters and Unicode

Total characters16836
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOther
2nd rowAsian
3rd rowWhite
4th rowWhite
5th rowWhite

Common Values

ValueCountFrequency (%)
Hispanic 612
20.4%
Other 609
20.3%
Asian 598
19.9%
White 598
19.9%
Black 583
19.4%

Length

2024-07-16T01:16:19.975906image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-16T01:16:20.248673image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
hispanic 612
20.4%
other 609
20.3%
asian 598
19.9%
white 598
19.9%
black 583
19.4%

Most occurring characters

ValueCountFrequency (%)
i 2420
14.4%
a 1793
10.6%
s 1210
 
7.2%
n 1210
 
7.2%
t 1207
 
7.2%
e 1207
 
7.2%
h 1207
 
7.2%
c 1195
 
7.1%
H 612
 
3.6%
p 612
 
3.6%
Other values (7) 4163
24.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13836
82.2%
Uppercase Letter 3000
 
17.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 2420
17.5%
a 1793
13.0%
s 1210
8.7%
n 1210
8.7%
t 1207
8.7%
e 1207
8.7%
h 1207
8.7%
c 1195
8.6%
p 612
 
4.4%
r 609
 
4.4%
Other values (2) 1166
8.4%
Uppercase Letter
ValueCountFrequency (%)
H 612
20.4%
O 609
20.3%
A 598
19.9%
W 598
19.9%
B 583
19.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 16836
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 2420
14.4%
a 1793
10.6%
s 1210
 
7.2%
n 1210
 
7.2%
t 1207
 
7.2%
e 1207
 
7.2%
h 1207
 
7.2%
c 1195
 
7.1%
H 612
 
3.6%
p 612
 
3.6%
Other values (7) 4163
24.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16836
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 2420
14.4%
a 1793
10.6%
s 1210
 
7.2%
n 1210
 
7.2%
t 1207
 
7.2%
e 1207
 
7.2%
h 1207
 
7.2%
c 1195
 
7.1%
H 612
 
3.6%
p 612
 
3.6%
Other values (7) 4163
24.7%

ZipCode
Categorical

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size23.6 KiB
30301
666 
73301
621 
90210
602 
60601
559 
10001
552 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters15000
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row73301
2nd row60601
3rd row90210
4th row10001
5th row10001

Common Values

ValueCountFrequency (%)
30301 666
22.2%
73301 621
20.7%
90210 602
20.1%
60601 559
18.6%
10001 552
18.4%

Length

2024-07-16T01:16:20.582400image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-16T01:16:20.849669image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
30301 666
22.2%
73301 621
20.7%
90210 602
20.1%
60601 559
18.6%
10001 552
18.4%

Most occurring characters

ValueCountFrequency (%)
0 5931
39.5%
1 3552
23.7%
3 2574
17.2%
6 1118
 
7.5%
7 621
 
4.1%
9 602
 
4.0%
2 602
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5931
39.5%
1 3552
23.7%
3 2574
17.2%
6 1118
 
7.5%
7 621
 
4.1%
9 602
 
4.0%
2 602
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5931
39.5%
1 3552
23.7%
3 2574
17.2%
6 1118
 
7.5%
7 621
 
4.1%
9 602
 
4.0%
2 602
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5931
39.5%
1 3552
23.7%
3 2574
17.2%
6 1118
 
7.5%
7 621
 
4.1%
9 602
 
4.0%
2 602
 
4.0%
Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size23.6 KiB
Fibromyalgia
633 
Cancer Pain
621 
Arthritis
585 
Post-Surgery Pain
583 
Chronic Back Pain
578 

Length

Max length17
Median length12
Mean length13.143
Min length9

Characters and Unicode

Total characters39429
Distinct characters25
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFibromyalgia
2nd rowCancer Pain
3rd rowFibromyalgia
4th rowFibromyalgia
5th rowPost-Surgery Pain

Common Values

ValueCountFrequency (%)
Fibromyalgia 633
21.1%
Cancer Pain 621
20.7%
Arthritis 585
19.5%
Post-Surgery Pain 583
19.4%
Chronic Back Pain 578
19.3%

Length

2024-07-16T01:16:21.213721image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-16T01:16:21.513024image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
pain 1782
33.2%
fibromyalgia 633
 
11.8%
cancer 621
 
11.6%
arthritis 585
 
10.9%
post-surgery 583
 
10.9%
chronic 578
 
10.8%
back 578
 
10.8%

Most occurring characters

ValueCountFrequency (%)
i 4796
12.2%
a 4247
 
10.8%
r 4168
 
10.6%
n 2981
 
7.6%
P 2365
 
6.0%
2360
 
6.0%
o 1794
 
4.5%
c 1777
 
4.5%
t 1753
 
4.4%
y 1216
 
3.1%
Other values (15) 11972
30.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 30543
77.5%
Uppercase Letter 5943
 
15.1%
Space Separator 2360
 
6.0%
Dash Punctuation 583
 
1.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 4796
15.7%
a 4247
13.9%
r 4168
13.6%
n 2981
9.8%
o 1794
 
5.9%
c 1777
 
5.8%
t 1753
 
5.7%
y 1216
 
4.0%
g 1216
 
4.0%
e 1204
 
3.9%
Other values (7) 5391
17.7%
Uppercase Letter
ValueCountFrequency (%)
P 2365
39.8%
C 1199
20.2%
F 633
 
10.7%
A 585
 
9.8%
S 583
 
9.8%
B 578
 
9.7%
Space Separator
ValueCountFrequency (%)
2360
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 583
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 36486
92.5%
Common 2943
 
7.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 4796
13.1%
a 4247
11.6%
r 4168
11.4%
n 2981
 
8.2%
P 2365
 
6.5%
o 1794
 
4.9%
c 1777
 
4.9%
t 1753
 
4.8%
y 1216
 
3.3%
g 1216
 
3.3%
Other values (13) 10173
27.9%
Common
ValueCountFrequency (%)
2360
80.2%
- 583
 
19.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39429
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 4796
12.2%
a 4247
 
10.8%
r 4168
 
10.6%
n 2981
 
7.6%
P 2365
 
6.0%
2360
 
6.0%
o 1794
 
4.5%
c 1777
 
4.5%
t 1753
 
4.4%
y 1216
 
3.1%
Other values (15) 11972
30.4%

NumOpioidPrescriptions
Real number (ℝ)

Distinct19
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.025667
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2024-07-16T01:16:21.796729image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median10
Q315
95-th percentile18
Maximum19
Range18
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.3812013
Coefficient of variation (CV)0.53674249
Kurtosis-1.1766549
Mean10.025667
Median Absolute Deviation (MAD)5
Skewness0.011644412
Sum30077
Variance28.957327
MonotonicityNot monotonic
2024-07-16T01:16:22.575507image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
10 194
 
6.5%
5 172
 
5.7%
6 171
 
5.7%
13 171
 
5.7%
18 169
 
5.6%
12 168
 
5.6%
16 168
 
5.6%
7 165
 
5.5%
3 158
 
5.3%
4 155
 
5.2%
Other values (9) 1309
43.6%
ValueCountFrequency (%)
1 138
4.6%
2 142
4.7%
3 158
5.3%
4 155
5.2%
5 172
5.7%
6 171
5.7%
7 165
5.5%
8 151
5.0%
9 149
5.0%
10 194
6.5%
ValueCountFrequency (%)
19 145
4.8%
18 169
5.6%
17 142
4.7%
16 168
5.6%
15 152
5.1%
14 141
4.7%
13 171
5.7%
12 168
5.6%
11 149
5.0%
10 194
6.5%

AverageDosage
Real number (ℝ)

Distinct95
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.089
Minimum5
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2024-07-16T01:16:22.930659image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile10
Q129
median52
Q376
95-th percentile95
Maximum99
Range94
Interquartile range (IQR)47

Descriptive statistics

Standard deviation27.392186
Coefficient of variation (CV)0.52587276
Kurtosis-1.2205004
Mean52.089
Median Absolute Deviation (MAD)24
Skewness0.034422999
Sum156267
Variance750.33186
MonotonicityNot monotonic
2024-07-16T01:16:23.319657image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33 44
 
1.5%
73 44
 
1.5%
21 42
 
1.4%
83 42
 
1.4%
34 41
 
1.4%
56 41
 
1.4%
31 41
 
1.4%
30 40
 
1.3%
86 40
 
1.3%
29 40
 
1.3%
Other values (85) 2585
86.2%
ValueCountFrequency (%)
5 21
0.7%
6 33
1.1%
7 29
1.0%
8 24
0.8%
9 36
1.2%
10 23
0.8%
11 31
1.0%
12 32
1.1%
13 28
0.9%
14 29
1.0%
ValueCountFrequency (%)
99 28
0.9%
98 34
1.1%
97 36
1.2%
96 35
1.2%
95 34
1.1%
94 32
1.1%
93 30
1.0%
92 36
1.2%
91 34
1.1%
90 25
0.8%

DurationOfPrescriptions
Real number (ℝ)

Distinct29
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.974
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2024-07-16T01:16:23.649489image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median15
Q322
95-th percentile28
Maximum29
Range28
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.3232055
Coefficient of variation (CV)0.55584383
Kurtosis-1.168202
Mean14.974
Median Absolute Deviation (MAD)7
Skewness0.009554749
Sum44922
Variance69.275749
MonotonicityNot monotonic
2024-07-16T01:16:24.009031image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
26 119
 
4.0%
10 118
 
3.9%
9 117
 
3.9%
11 116
 
3.9%
29 115
 
3.8%
16 114
 
3.8%
6 113
 
3.8%
1 112
 
3.7%
14 110
 
3.7%
20 109
 
3.6%
Other values (19) 1857
61.9%
ValueCountFrequency (%)
1 112
3.7%
2 109
3.6%
3 96
3.2%
4 83
2.8%
5 94
3.1%
6 113
3.8%
7 96
3.2%
8 94
3.1%
9 117
3.9%
10 118
3.9%
ValueCountFrequency (%)
29 115
3.8%
28 92
3.1%
27 90
3.0%
26 119
4.0%
25 105
3.5%
24 84
2.8%
23 107
3.6%
22 103
3.4%
21 95
3.2%
20 109
3.6%

NumHealthcareVisits
Real number (ℝ)

ZEROS 

Distinct20
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.5823333
Minimum0
Maximum19
Zeros164
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2024-07-16T01:16:24.325053image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median10
Q315
95-th percentile19
Maximum19
Range19
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.7690249
Coefficient of variation (CV)0.60204803
Kurtosis-1.1987407
Mean9.5823333
Median Absolute Deviation (MAD)5
Skewness-0.029814822
Sum28747
Variance33.281648
MonotonicityNot monotonic
2024-07-16T01:16:24.669602image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
15 180
 
6.0%
0 164
 
5.5%
8 162
 
5.4%
10 158
 
5.3%
3 158
 
5.3%
6 157
 
5.2%
19 154
 
5.1%
12 154
 
5.1%
5 149
 
5.0%
18 148
 
4.9%
Other values (10) 1416
47.2%
ValueCountFrequency (%)
0 164
5.5%
1 134
4.5%
2 140
4.7%
3 158
5.3%
4 132
4.4%
5 149
5.0%
6 157
5.2%
7 137
4.6%
8 162
5.4%
9 141
4.7%
ValueCountFrequency (%)
19 154
5.1%
18 148
4.9%
17 147
4.9%
16 147
4.9%
15 180
6.0%
14 147
4.9%
13 146
4.9%
12 154
5.1%
11 145
4.8%
10 158
5.3%
Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size23.6 KiB
1
613 
3
607 
2
603 
4
589 
0
588 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row2
4th row2
5th row3

Common Values

ValueCountFrequency (%)
1 613
20.4%
3 607
20.2%
2 603
20.1%
4 589
19.6%
0 588
19.6%

Length

2024-07-16T01:16:25.026819image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-16T01:16:25.297396image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1 613
20.4%
3 607
20.2%
2 603
20.1%
4 589
19.6%
0 588
19.6%

Most occurring characters

ValueCountFrequency (%)
1 613
20.4%
3 607
20.2%
2 603
20.1%
4 589
19.6%
0 588
19.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 613
20.4%
3 607
20.2%
2 603
20.1%
4 589
19.6%
0 588
19.6%

Most occurring scripts

ValueCountFrequency (%)
Common 3000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 613
20.4%
3 607
20.2%
2 603
20.1%
4 589
19.6%
0 588
19.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 613
20.4%
3 607
20.2%
2 603
20.1%
4 589
19.6%
0 588
19.6%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
False
2411 
True
589 
ValueCountFrequency (%)
False 2411
80.4%
True 589
 
19.6%
2024-07-16T01:16:25.534965image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Distinct1355
Distinct (%)45.2%
Missing0
Missing (%)0.0%
Memory size23.6 KiB
Minimum2020-01-01 00:00:00
Maximum2024-05-23 00:00:00
2024-07-16T01:16:25.809571image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:26.198831image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

MedicationName
Categorical

Distinct12
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size23.6 KiB
Morphine
283 
Tramadol
272 
Oxycodone
263 
Tapentadol
263 
Meperidine
260 
Other values (7)
1659 

Length

Max length13
Median length10
Mean length9.69
Min length7

Characters and Unicode

Total characters29070
Distinct characters23
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHydrocodone
2nd rowHydromorphone
3rd rowHydrocodone
4th rowOxymorphone
5th rowTramadol

Common Values

ValueCountFrequency (%)
Morphine 283
9.4%
Tramadol 272
9.1%
Oxycodone 263
8.8%
Tapentadol 263
8.8%
Meperidine 260
8.7%
Methadone 258
8.6%
Codeine 246
8.2%
Buprenorphine 245
8.2%
Hydrocodone 244
8.1%
Hydromorphone 224
7.5%
Other values (2) 442
14.7%

Length

2024-07-16T01:16:26.599729image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
morphine 283
9.4%
tramadol 272
9.1%
oxycodone 263
8.8%
tapentadol 263
8.8%
meperidine 260
8.7%
methadone 258
8.6%
codeine 246
8.2%
buprenorphine 245
8.2%
hydrocodone 244
8.1%
hydromorphone 224
7.5%
Other values (2) 442
14.7%

Most occurring characters

ValueCountFrequency (%)
e 3997
13.7%
o 3945
13.6%
n 3191
11.0%
d 2274
 
7.8%
r 2221
 
7.6%
p 1744
 
6.0%
a 1546
 
5.3%
i 1294
 
4.5%
h 1234
 
4.2%
y 1173
 
4.0%
Other values (13) 6451
22.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 26070
89.7%
Uppercase Letter 3000
 
10.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3997
15.3%
o 3945
15.1%
n 3191
12.2%
d 2274
8.7%
r 2221
8.5%
p 1744
6.7%
a 1546
 
5.9%
i 1294
 
5.0%
h 1234
 
4.7%
y 1173
 
4.5%
Other values (6) 3451
13.2%
Uppercase Letter
ValueCountFrequency (%)
M 801
26.7%
T 535
17.8%
O 487
16.2%
H 468
15.6%
C 246
 
8.2%
B 245
 
8.2%
F 218
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 29070
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 3997
13.7%
o 3945
13.6%
n 3191
11.0%
d 2274
 
7.8%
r 2221
 
7.6%
p 1744
 
6.0%
a 1546
 
5.3%
i 1294
 
4.5%
h 1234
 
4.2%
y 1173
 
4.0%
Other values (13) 6451
22.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29070
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 3997
13.7%
o 3945
13.6%
n 3191
11.0%
d 2274
 
7.8%
r 2221
 
7.6%
p 1744
 
6.0%
a 1546
 
5.3%
i 1294
 
4.5%
h 1234
 
4.2%
y 1173
 
4.0%
Other values (13) 6451
22.2%

Dosage
Categorical

Distinct12
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size23.6 KiB
10 mg
459 
80 mg
250 
40 mg
244 
75 mcg/hour
243 
20 mg
237 
Other values (7)
1567 

Length

Max length13
Median length5
Mean length7.0466667
Min length4

Characters and Unicode

Total characters21140
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10 mg
2nd row100 mcg/hour
3rd row20 mg
4th row10 mg
5th row80 mg

Common Values

ValueCountFrequency (%)
10 mg 459
15.3%
80 mg 250
8.3%
40 mg 244
8.1%
75 mcg/hour 243
8.1%
20 mg 237
7.9%
60 mg 236
7.9%
50 mcg/hour 233
7.8%
2.5 mg 231
7.7%
100 mcg/hour 218
7.3%
12.5 mcg/hour 218
7.3%
Other values (2) 431
14.4%

Length

2024-07-16T01:16:26.955861image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mg 2088
34.8%
mcg/hour 912
15.2%
10 459
 
7.6%
80 250
 
4.2%
40 244
 
4.1%
75 243
 
4.0%
20 237
 
4.0%
60 236
 
3.9%
50 233
 
3.9%
2.5 231
 
3.9%
Other values (4) 867
14.4%

Most occurring characters

ValueCountFrequency (%)
3000
14.2%
m 3000
14.2%
g 3000
14.2%
0 2309
10.9%
5 1142
 
5.4%
c 912
 
4.3%
h 912
 
4.3%
r 912
 
4.3%
u 912
 
4.3%
o 912
 
4.3%
Other values (9) 4129
19.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10560
50.0%
Decimal Number 6219
29.4%
Space Separator 3000
 
14.2%
Other Punctuation 1361
 
6.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2309
37.1%
5 1142
18.4%
1 895
 
14.4%
2 686
 
11.0%
8 250
 
4.0%
4 244
 
3.9%
7 243
 
3.9%
6 236
 
3.8%
3 214
 
3.4%
Lowercase Letter
ValueCountFrequency (%)
m 3000
28.4%
g 3000
28.4%
c 912
 
8.6%
h 912
 
8.6%
r 912
 
8.6%
u 912
 
8.6%
o 912
 
8.6%
Other Punctuation
ValueCountFrequency (%)
/ 912
67.0%
. 449
33.0%
Space Separator
ValueCountFrequency (%)
3000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10580
50.0%
Latin 10560
50.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3000
28.4%
0 2309
21.8%
5 1142
 
10.8%
/ 912
 
8.6%
1 895
 
8.5%
2 686
 
6.5%
. 449
 
4.2%
8 250
 
2.4%
4 244
 
2.3%
7 243
 
2.3%
Other values (2) 450
 
4.3%
Latin
ValueCountFrequency (%)
m 3000
28.4%
g 3000
28.4%
c 912
 
8.6%
h 912
 
8.6%
r 912
 
8.6%
u 912
 
8.6%
o 912
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21140
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3000
14.2%
m 3000
14.2%
g 3000
14.2%
0 2309
10.9%
5 1142
 
5.4%
c 912
 
4.3%
h 912
 
4.3%
r 912
 
4.3%
u 912
 
4.3%
o 912
 
4.3%
Other values (9) 4129
19.5%

Frequency
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.6 KiB
every 8 hours
763 
once daily
761 
every 12 hours
753 
every 4-6 hours
723 

Length

Max length15
Median length14
Mean length12.972
Min length10

Characters and Unicode

Total characters38916
Distinct characters21
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowevery 8 hours
2nd rowevery 8 hours
3rd rowevery 4-6 hours
4th rowevery 12 hours
5th rowevery 4-6 hours

Common Values

ValueCountFrequency (%)
every 8 hours 763
25.4%
once daily 761
25.4%
every 12 hours 753
25.1%
every 4-6 hours 723
24.1%

Length

2024-07-16T01:16:27.297967image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-16T01:16:27.565406image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
every 2239
27.2%
hours 2239
27.2%
8 763
 
9.3%
once 761
 
9.2%
daily 761
 
9.2%
12 753
 
9.1%
4-6 723
 
8.8%

Most occurring characters

ValueCountFrequency (%)
e 5239
13.5%
5239
13.5%
r 4478
11.5%
y 3000
 
7.7%
o 3000
 
7.7%
h 2239
 
5.8%
u 2239
 
5.8%
s 2239
 
5.8%
v 2239
 
5.8%
8 763
 
2.0%
Other values (11) 8241
21.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 29239
75.1%
Space Separator 5239
 
13.5%
Decimal Number 3715
 
9.5%
Dash Punctuation 723
 
1.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 5239
17.9%
r 4478
15.3%
y 3000
10.3%
o 3000
10.3%
h 2239
7.7%
u 2239
7.7%
s 2239
7.7%
v 2239
7.7%
a 761
 
2.6%
l 761
 
2.6%
Other values (4) 3044
10.4%
Decimal Number
ValueCountFrequency (%)
8 763
20.5%
1 753
20.3%
2 753
20.3%
4 723
19.5%
6 723
19.5%
Space Separator
ValueCountFrequency (%)
5239
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 723
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 29239
75.1%
Common 9677
 
24.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5239
17.9%
r 4478
15.3%
y 3000
10.3%
o 3000
10.3%
h 2239
7.7%
u 2239
7.7%
s 2239
7.7%
v 2239
7.7%
a 761
 
2.6%
l 761
 
2.6%
Other values (4) 3044
10.4%
Common
ValueCountFrequency (%)
5239
54.1%
8 763
 
7.9%
1 753
 
7.8%
2 753
 
7.8%
4 723
 
7.5%
- 723
 
7.5%
6 723
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 38916
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 5239
13.5%
5239
13.5%
r 4478
11.5%
y 3000
 
7.7%
o 3000
 
7.7%
h 2239
 
5.8%
u 2239
 
5.8%
s 2239
 
5.8%
v 2239
 
5.8%
8 763
 
2.0%
Other values (11) 8241
21.2%

Duration
Real number (ℝ)

Distinct29
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.041
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2024-07-16T01:16:27.854979image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median15
Q322
95-th percentile28
Maximum29
Range28
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.3515209
Coefficient of variation (CV)0.55525038
Kurtosis-1.2053241
Mean15.041
Median Absolute Deviation (MAD)7
Skewness-0.0032739989
Sum45123
Variance69.747902
MonotonicityNot monotonic
2024-07-16T01:16:28.211540image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
23 128
 
4.3%
17 119
 
4.0%
21 116
 
3.9%
4 115
 
3.8%
12 114
 
3.8%
24 113
 
3.8%
2 113
 
3.8%
10 112
 
3.7%
7 110
 
3.7%
29 110
 
3.7%
Other values (19) 1850
61.7%
ValueCountFrequency (%)
1 90
3.0%
2 113
3.8%
3 97
3.2%
4 115
3.8%
5 99
3.3%
6 98
3.3%
7 110
3.7%
8 105
3.5%
9 93
3.1%
10 112
3.7%
ValueCountFrequency (%)
29 110
3.7%
28 101
3.4%
27 93
3.1%
26 107
3.6%
25 90
3.0%
24 113
3.8%
23 128
4.3%
22 90
3.0%
21 116
3.9%
20 105
3.5%

Refills
Categorical

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size23.6 KiB
0
637 
4
617 
2
591 
3
580 
1
575 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row2
4th row0
5th row2

Common Values

ValueCountFrequency (%)
0 637
21.2%
4 617
20.6%
2 591
19.7%
3 580
19.3%
1 575
19.2%

Length

2024-07-16T01:16:28.571992image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-16T01:16:28.833431image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 637
21.2%
4 617
20.6%
2 591
19.7%
3 580
19.3%
1 575
19.2%

Most occurring characters

ValueCountFrequency (%)
0 637
21.2%
4 617
20.6%
2 591
19.7%
3 580
19.3%
1 575
19.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 637
21.2%
4 617
20.6%
2 591
19.7%
3 580
19.3%
1 575
19.2%

Most occurring scripts

ValueCountFrequency (%)
Common 3000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 637
21.2%
4 617
20.6%
2 591
19.7%
3 580
19.3%
1 575
19.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 637
21.2%
4 617
20.6%
2 591
19.7%
3 580
19.3%
1 575
19.2%

MedicationClass
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.6 KiB
Analgesic
1029 
Narcotic
1014 
Opioid
957 

Length

Max length9
Median length8
Mean length7.705
Min length6

Characters and Unicode

Total characters23115
Distinct characters16
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOpioid
2nd rowNarcotic
3rd rowAnalgesic
4th rowAnalgesic
5th rowAnalgesic

Common Values

ValueCountFrequency (%)
Analgesic 1029
34.3%
Narcotic 1014
33.8%
Opioid 957
31.9%

Length

2024-07-16T01:16:29.171087image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-16T01:16:29.425952image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
analgesic 1029
34.3%
narcotic 1014
33.8%
opioid 957
31.9%

Most occurring characters

ValueCountFrequency (%)
i 3957
17.1%
c 3057
13.2%
a 2043
 
8.8%
o 1971
 
8.5%
A 1029
 
4.5%
n 1029
 
4.5%
l 1029
 
4.5%
g 1029
 
4.5%
e 1029
 
4.5%
s 1029
 
4.5%
Other values (6) 5913
25.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 20115
87.0%
Uppercase Letter 3000
 
13.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 3957
19.7%
c 3057
15.2%
a 2043
10.2%
o 1971
9.8%
n 1029
 
5.1%
l 1029
 
5.1%
g 1029
 
5.1%
e 1029
 
5.1%
s 1029
 
5.1%
r 1014
 
5.0%
Other values (3) 2928
14.6%
Uppercase Letter
ValueCountFrequency (%)
A 1029
34.3%
N 1014
33.8%
O 957
31.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 23115
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 3957
17.1%
c 3057
13.2%
a 2043
 
8.8%
o 1971
 
8.5%
A 1029
 
4.5%
n 1029
 
4.5%
l 1029
 
4.5%
g 1029
 
4.5%
e 1029
 
4.5%
s 1029
 
4.5%
Other values (6) 5913
25.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23115
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 3957
17.1%
c 3057
13.2%
a 2043
 
8.8%
o 1971
 
8.5%
A 1029
 
4.5%
n 1029
 
4.5%
l 1029
 
4.5%
g 1029
 
4.5%
e 1029
 
4.5%
s 1029
 
4.5%
Other values (6) 5913
25.6%

Adherence
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.6 KiB
High
1017 
Low
992 
Moderate
991 

Length

Max length8
Median length4
Mean length4.9906667
Min length3

Characters and Unicode

Total characters14972
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowModerate
2nd rowLow
3rd rowModerate
4th rowLow
5th rowHigh

Common Values

ValueCountFrequency (%)
High 1017
33.9%
Low 992
33.1%
Moderate 991
33.0%

Length

2024-07-16T01:16:29.729161image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-16T01:16:29.973998image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
high 1017
33.9%
low 992
33.1%
moderate 991
33.0%

Most occurring characters

ValueCountFrequency (%)
o 1983
13.2%
e 1982
13.2%
H 1017
 
6.8%
i 1017
 
6.8%
g 1017
 
6.8%
h 1017
 
6.8%
L 992
 
6.6%
w 992
 
6.6%
M 991
 
6.6%
d 991
 
6.6%
Other values (3) 2973
19.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 11972
80.0%
Uppercase Letter 3000
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 1983
16.6%
e 1982
16.6%
i 1017
8.5%
g 1017
8.5%
h 1017
8.5%
w 992
8.3%
d 991
8.3%
r 991
8.3%
a 991
8.3%
t 991
8.3%
Uppercase Letter
ValueCountFrequency (%)
H 1017
33.9%
L 992
33.1%
M 991
33.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 14972
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 1983
13.2%
e 1982
13.2%
H 1017
 
6.8%
i 1017
 
6.8%
g 1017
 
6.8%
h 1017
 
6.8%
L 992
 
6.6%
w 992
 
6.6%
M 991
 
6.6%
d 991
 
6.6%
Other values (3) 2973
19.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14972
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 1983
13.2%
e 1982
13.2%
H 1017
 
6.8%
i 1017
 
6.8%
g 1017
 
6.8%
h 1017
 
6.8%
L 992
 
6.6%
w 992
 
6.6%
M 991
 
6.6%
d 991
 
6.6%
Other values (3) 2973
19.9%

ClinicalNotes
Categorical

Distinct11
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size23.6 KiB
Recovering from injury, prescribed Codeine.
300 
Post-operative pain managed with Hydrocodone.
290 
Patient with previous substance abuse, now on Methadone treatment.
280 
Patient reports effective pain relief with Tapentadol.
276 
Patient reports chronic back pain, prescribed Fentanyl patch.
274 
Other values (6)
1580 

Length

Max length66
Median length54
Mean length51.982333
Min length39

Characters and Unicode

Total characters155947
Distinct characters38
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPost-operative pain managed with Hydrocodone.
2nd rowPrescribed Oxymorphone for severe pain.
3rd rowPatient reports effective pain relief with Tapentadol.
4th rowUsing Tramadol for moderate pain management.
5th rowPatient reports effective pain relief with Tapentadol.

Common Values

ValueCountFrequency (%)
Recovering from injury, prescribed Codeine. 300
10.0%
Post-operative pain managed with Hydrocodone. 290
9.7%
Patient with previous substance abuse, now on Methadone treatment. 280
9.3%
Patient reports effective pain relief with Tapentadol. 276
9.2%
Patient reports chronic back pain, prescribed Fentanyl patch. 274
9.1%
History of depression, taking Oxycodone for post-surgery pain. 271
9.0%
Prescribed Oxymorphone for severe pain. 269
9.0%
Using Tramadol for moderate pain management. 269
9.0%
Meperidine prescribed for acute pain episodes. 264
8.8%
Chronic arthritis, using Hydromorphone for pain relief. 260
8.7%

Length

2024-07-16T01:16:30.282028image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pain 2420
 
12.2%
prescribed 1354
 
6.8%
for 1333
 
6.7%
with 846
 
4.3%
patient 830
 
4.2%
reports 550
 
2.8%
from 547
 
2.8%
relief 536
 
2.7%
chronic 534
 
2.7%
using 529
 
2.7%
Other values (37) 10375
52.3%

Most occurring characters

ValueCountFrequency (%)
e 17667
 
11.3%
16854
 
10.8%
r 12950
 
8.3%
i 11661
 
7.5%
n 11149
 
7.1%
o 10403
 
6.7%
a 9001
 
5.8%
t 8271
 
5.3%
p 7268
 
4.7%
s 6502
 
4.2%
Other values (28) 44221
28.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 128164
82.2%
Space Separator 16854
 
10.8%
Uppercase Letter 5736
 
3.7%
Other Punctuation 4632
 
3.0%
Dash Punctuation 561
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 17667
13.8%
r 12950
10.1%
i 11661
9.1%
n 11149
 
8.7%
o 10403
 
8.1%
a 9001
 
7.0%
t 8271
 
6.5%
p 7268
 
5.7%
s 6502
 
5.1%
c 5132
 
4.0%
Other values (14) 28160
22.0%
Uppercase Letter
ValueCountFrequency (%)
P 1389
24.2%
H 821
14.3%
M 791
13.8%
C 560
9.8%
T 545
 
9.5%
O 540
 
9.4%
R 300
 
5.2%
F 274
 
4.8%
U 269
 
4.7%
E 247
 
4.3%
Other Punctuation
ValueCountFrequency (%)
. 3000
64.8%
, 1632
35.2%
Space Separator
ValueCountFrequency (%)
16854
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 561
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 133900
85.9%
Common 22047
 
14.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 17667
13.2%
r 12950
 
9.7%
i 11661
 
8.7%
n 11149
 
8.3%
o 10403
 
7.8%
a 9001
 
6.7%
t 8271
 
6.2%
p 7268
 
5.4%
s 6502
 
4.9%
c 5132
 
3.8%
Other values (24) 33896
25.3%
Common
ValueCountFrequency (%)
16854
76.4%
. 3000
 
13.6%
, 1632
 
7.4%
- 561
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 155947
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 17667
 
11.3%
16854
 
10.8%
r 12950
 
8.3%
i 11661
 
7.5%
n 11149
 
7.1%
o 10403
 
6.7%
a 9001
 
5.8%
t 8271
 
5.3%
p 7268
 
4.7%
s 6502
 
4.2%
Other values (28) 44221
28.4%

Specialty
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.6 KiB
Pain Management
765 
Primary Care
750 
Orthopedics
746 
Oncology
739 

Length

Max length15
Median length12
Mean length11.531
Min length8

Characters and Unicode

Total characters34593
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOrthopedics
2nd rowPain Management
3rd rowOncology
4th rowOrthopedics
5th rowOncology

Common Values

ValueCountFrequency (%)
Pain Management 765
25.5%
Primary Care 750
25.0%
Orthopedics 746
24.9%
Oncology 739
24.6%

Length

2024-07-16T01:16:30.635797image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-16T01:16:30.913417image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
pain 765
16.9%
management 765
16.9%
primary 750
16.6%
care 750
16.6%
orthopedics 746
16.5%
oncology 739
16.4%

Most occurring characters

ValueCountFrequency (%)
a 3795
 
11.0%
n 3034
 
8.8%
e 3026
 
8.7%
r 2996
 
8.7%
i 2261
 
6.5%
o 2224
 
6.4%
m 1515
 
4.4%
P 1515
 
4.4%
1515
 
4.4%
t 1511
 
4.4%
Other values (11) 11201
32.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 28563
82.6%
Uppercase Letter 4515
 
13.1%
Space Separator 1515
 
4.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 3795
13.3%
n 3034
10.6%
e 3026
10.6%
r 2996
10.5%
i 2261
7.9%
o 2224
7.8%
m 1515
 
5.3%
t 1511
 
5.3%
g 1504
 
5.3%
y 1489
 
5.2%
Other values (6) 5208
18.2%
Uppercase Letter
ValueCountFrequency (%)
P 1515
33.6%
O 1485
32.9%
M 765
16.9%
C 750
16.6%
Space Separator
ValueCountFrequency (%)
1515
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 33078
95.6%
Common 1515
 
4.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 3795
 
11.5%
n 3034
 
9.2%
e 3026
 
9.1%
r 2996
 
9.1%
i 2261
 
6.8%
o 2224
 
6.7%
m 1515
 
4.6%
P 1515
 
4.6%
t 1511
 
4.6%
g 1504
 
4.5%
Other values (10) 9697
29.3%
Common
ValueCountFrequency (%)
1515
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34593
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 3795
 
11.0%
n 3034
 
8.8%
e 3026
 
8.7%
r 2996
 
8.7%
i 2261
 
6.5%
o 2224
 
6.4%
m 1515
 
4.4%
P 1515
 
4.4%
1515
 
4.4%
t 1511
 
4.4%
Other values (11) 11201
32.4%

AppointmentType
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.6 KiB
Consultation
1061 
Follow-up
1007 
Routine Check-up
932 

Length

Max length16
Median length12
Mean length12.235667
Min length9

Characters and Unicode

Total characters36707
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRoutine Check-up
2nd rowConsultation
3rd rowRoutine Check-up
4th rowConsultation
5th rowFollow-up

Common Values

ValueCountFrequency (%)
Consultation 1061
35.4%
Follow-up 1007
33.6%
Routine Check-up 932
31.1%

Length

2024-07-16T01:16:31.259654image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-16T01:16:31.528189image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
consultation 1061
27.0%
follow-up 1007
25.6%
routine 932
23.7%
check-up 932
23.7%

Most occurring characters

ValueCountFrequency (%)
o 5068
13.8%
u 3932
10.7%
l 3075
 
8.4%
n 3054
 
8.3%
t 3054
 
8.3%
C 1993
 
5.4%
i 1993
 
5.4%
p 1939
 
5.3%
- 1939
 
5.3%
e 1864
 
5.1%
Other values (9) 8796
24.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 29904
81.5%
Uppercase Letter 3932
 
10.7%
Dash Punctuation 1939
 
5.3%
Space Separator 932
 
2.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 5068
16.9%
u 3932
13.1%
l 3075
10.3%
n 3054
10.2%
t 3054
10.2%
i 1993
 
6.7%
p 1939
 
6.5%
e 1864
 
6.2%
s 1061
 
3.5%
a 1061
 
3.5%
Other values (4) 3803
12.7%
Uppercase Letter
ValueCountFrequency (%)
C 1993
50.7%
F 1007
25.6%
R 932
23.7%
Dash Punctuation
ValueCountFrequency (%)
- 1939
100.0%
Space Separator
ValueCountFrequency (%)
932
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 33836
92.2%
Common 2871
 
7.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 5068
15.0%
u 3932
11.6%
l 3075
9.1%
n 3054
9.0%
t 3054
9.0%
C 1993
 
5.9%
i 1993
 
5.9%
p 1939
 
5.7%
e 1864
 
5.5%
s 1061
 
3.1%
Other values (7) 6803
20.1%
Common
ValueCountFrequency (%)
- 1939
67.5%
932
32.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36707
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 5068
13.8%
u 3932
10.7%
l 3075
 
8.4%
n 3054
 
8.3%
t 3054
 
8.3%
C 1993
 
5.4%
i 1993
 
5.4%
p 1939
 
5.3%
- 1939
 
5.3%
e 1864
 
5.1%
Other values (9) 8796
24.0%

SubSpecialty
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.6 KiB
Specialized
1532 
General
1468 

Length

Max length11
Median length11
Mean length9.0426667
Min length7

Characters and Unicode

Total characters27128
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSpecialized
2nd rowGeneral
3rd rowSpecialized
4th rowSpecialized
5th rowGeneral

Common Values

ValueCountFrequency (%)
Specialized 1532
51.1%
General 1468
48.9%

Length

2024-07-16T01:16:31.865080image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-16T01:16:32.133552image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
specialized 1532
51.1%
general 1468
48.9%

Most occurring characters

ValueCountFrequency (%)
e 6000
22.1%
i 3064
11.3%
a 3000
11.1%
l 3000
11.1%
S 1532
 
5.6%
p 1532
 
5.6%
c 1532
 
5.6%
z 1532
 
5.6%
d 1532
 
5.6%
G 1468
 
5.4%
Other values (2) 2936
10.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 24128
88.9%
Uppercase Letter 3000
 
11.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6000
24.9%
i 3064
12.7%
a 3000
12.4%
l 3000
12.4%
p 1532
 
6.3%
c 1532
 
6.3%
z 1532
 
6.3%
d 1532
 
6.3%
n 1468
 
6.1%
r 1468
 
6.1%
Uppercase Letter
ValueCountFrequency (%)
S 1532
51.1%
G 1468
48.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 27128
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6000
22.1%
i 3064
11.3%
a 3000
11.1%
l 3000
11.1%
S 1532
 
5.6%
p 1532
 
5.6%
c 1532
 
5.6%
z 1532
 
5.6%
d 1532
 
5.6%
G 1468
 
5.4%
Other values (2) 2936
10.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27128
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6000
22.1%
i 3064
11.3%
a 3000
11.1%
l 3000
11.1%
S 1532
 
5.6%
p 1532
 
5.6%
c 1532
 
5.6%
z 1532
 
5.6%
d 1532
 
5.6%
G 1468
 
5.4%
Other values (2) 2936
10.8%
Distinct2945
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Memory size23.6 KiB
Minimum2024-07-16 00:00:08
Maximum2024-07-16 23:59:57
2024-07-16T01:16:32.412319image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:32.776640image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2935
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Memory size23.6 KiB
Minimum2024-07-16 00:00:54
Maximum2024-07-16 23:59:43
2024-07-16T01:16:33.107144image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:33.463418image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct50
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.008
Minimum10
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2024-07-16T01:16:33.811256image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile12
Q123
median35
Q347
95-th percentile57
Maximum59
Range49
Interquartile range (IQR)24

Descriptive statistics

Standard deviation14.271621
Coefficient of variation (CV)0.40766742
Kurtosis-1.17811
Mean35.008
Median Absolute Deviation (MAD)12
Skewness-0.036898559
Sum105024
Variance203.67916
MonotonicityNot monotonic
2024-07-16T01:16:34.205536image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43 78
 
2.6%
47 74
 
2.5%
26 73
 
2.4%
34 72
 
2.4%
54 71
 
2.4%
52 70
 
2.3%
59 70
 
2.3%
19 68
 
2.3%
50 67
 
2.2%
20 67
 
2.2%
Other values (40) 2290
76.3%
ValueCountFrequency (%)
10 55
1.8%
11 51
1.7%
12 54
1.8%
13 48
1.6%
14 54
1.8%
15 64
2.1%
16 63
2.1%
17 50
1.7%
18 48
1.6%
19 68
2.3%
ValueCountFrequency (%)
59 70
2.3%
58 60
2.0%
57 58
1.9%
56 52
1.7%
55 56
1.9%
54 71
2.4%
53 57
1.9%
52 70
2.3%
51 58
1.9%
50 67
2.2%

Interactions

2024-07-16T01:16:14.045550image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:15:59.856118image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:02.432533image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2024-07-16T01:16:08.882157image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:10.991625image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2024-07-16T01:16:14.225399image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2024-07-16T01:16:02.707199image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:04.846936image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:07.023996image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:09.153356image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:11.190533image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:12.742923image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:14.495178image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:00.406921image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:02.976129image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:05.116202image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:07.285858image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:09.424839image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:11.389843image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:12.929317image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:14.788574image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:00.706686image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:03.249717image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:05.392342image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:07.552758image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:09.701946image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:11.585719image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:13.124899image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:15.059176image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:01.290898image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:03.503193image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:05.656640image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:07.796653image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:09.960377image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:11.769386image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:13.304392image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:15.333112image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:01.583995image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:03.775110image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:05.935299image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:08.067029image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:10.234818image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:11.964720image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:13.486979image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:15.617535image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:01.884063image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:04.050560image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:06.220034image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:08.348283image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:10.527285image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:12.172855image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:13.679923image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:15.887727image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:02.163103image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:04.314861image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:06.492290image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:08.621840image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:10.802440image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:12.364515image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-16T01:16:13.863970image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Missing values

2024-07-16T01:16:16.357326image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-16T01:16:17.228053image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PatientIDAgeGenderRaceZipCodeChronicPainConditionsNumOpioidPrescriptionsAverageDosageDurationOfPrescriptionsNumHealthcareVisitsNumHospitalizationsPainManagementTreatmentPrescriptionDateMedicationNameDosageFrequencyDurationRefillsMedicationClassAdherenceClinicalNotesSpecialtyAppointmentTypeSubSpecialtyTimeofAppointmentTimeSeenbyPhysicianTotalTimeSpentwithPhysician
0447964788423054FemaleOther73301Fibromyalgia6807180No2023-12-06Hydrocodone10 mgevery 8 hours10OpioidModeratePost-operative pain managed with Hydrocodone.OrthopedicsRoutine Check-upSpecialized13:39:0216:33:5936
1635462978234721FemaleAsian60601Cancer Pain117025120Yes2022-02-21Hydromorphone100 mcg/hourevery 8 hours200NarcoticLowPrescribed Oxymorphone for severe pain.Pain ManagementConsultationGeneral19:44:4617:33:3751
2466507743822267FemaleWhite90210Fibromyalgia5157102No2023-05-01Hydrocodone20 mgevery 4-6 hours72AnalgesicModeratePatient reports effective pain relief with Tapentadol.OncologyRoutine Check-upSpecialized18:05:3404:39:2846
3284476128519541MaleWhite10001Fibromyalgia169419142No2020-03-04Oxymorphone10 mgevery 12 hours280AnalgesicLowUsing Tramadol for moderate pain management.OrthopedicsConsultationSpecialized15:16:1115:52:5116
4478539117787136MaleWhite10001Post-Surgery Pain722693Yes2020-09-19Tramadol80 mgevery 4-6 hours252AnalgesicHighPatient reports effective pain relief with Tapentadol.OncologyFollow-upGeneral22:17:0502:17:3314
5362545751961453FemaleBlack10001Chronic Back Pain5891184No2021-06-28Hydromorphone60 mgonce daily94AnalgesicLowPatient with previous substance abuse, now on Methadone treatment.OncologyConsultationSpecialized01:36:3519:34:3624
6932340679911077MaleBlack73301Post-Surgery Pain11562982Yes2020-08-20Codeine60 mgevery 8 hours281AnalgesicModerateUsing Tramadol for moderate pain management.OrthopedicsConsultationGeneral16:56:5302:55:4051
7690291879115256FemaleHispanic60601Post-Surgery Pain155011193No2020-09-05Morphine80 mgevery 12 hours261NarcoticHighPatient with previous substance abuse, now on Methadone treatment.OncologyConsultationGeneral09:17:5923:23:2517
843435805198627FemaleHispanic10001Fibromyalgia348704No2020-09-08Buprenorphine100 mcg/hourevery 12 hours171AnalgesicModeratePatient reports effective pain relief with Tapentadol.OrthopedicsConsultationGeneral15:39:2822:41:0118
9949938067611942MaleHispanic10001Fibromyalgia5262160Yes2021-12-17Oxycodone12.5 mcg/hourevery 8 hours214NarcoticLowPrescribed Oxymorphone for severe pain.OncologyConsultationGeneral12:51:1304:39:4520
PatientIDAgeGenderRaceZipCodeChronicPainConditionsNumOpioidPrescriptionsAverageDosageDurationOfPrescriptionsNumHealthcareVisitsNumHospitalizationsPainManagementTreatmentPrescriptionDateMedicationNameDosageFrequencyDurationRefillsMedicationClassAdherenceClinicalNotesSpecialtyAppointmentTypeSubSpecialtyTimeofAppointmentTimeSeenbyPhysicianTotalTimeSpentwithPhysician
2990161253661610624FemaleBlack60601Chronic Back Pain16112722Yes2020-08-15Hydromorphone80 mgonce daily70NarcoticHighHistory of depression, taking Oxycodone for post-surgery pain.Pain ManagementConsultationSpecialized04:08:1504:02:3937
2991347675022379777MaleHispanic30301Chronic Back Pain19691560No2023-11-30Morphine100 mcg/hourevery 12 hours14OpioidHighExperiencing severe pain from cancer, prescribed Morphine.OrthopedicsConsultationSpecialized01:16:3417:47:0931
2992384462067150238FemaleOther30301Arthritis10281024No2023-01-11Morphine80 mgevery 4-6 hours243AnalgesicHighMeperidine prescribed for acute pain episodes.OrthopedicsConsultationGeneral04:57:3604:35:2950
2993519075942279855MaleWhite73301Post-Surgery Pain16494164No2024-02-03Morphine5 mgevery 8 hours122OpioidLowPatient reports effective pain relief with Tapentadol.Pain ManagementConsultationGeneral17:27:1717:45:2922
2994341093939453360MaleHispanic73301Post-Surgery Pain5652173No2021-04-11Methadone30 mgevery 8 hours50NarcoticLowPrescribed Oxymorphone for severe pain.OncologyConsultationSpecialized07:15:3513:42:5850
2995965726322638166MaleBlack10001Chronic Back Pain162328182No2021-06-18Hydromorphone60 mgevery 4-6 hours93OpioidModerateChronic arthritis, using Hydromorphone for pain relief.OrthopedicsConsultationSpecialized10:37:4222:54:3956
2996836170625615668FemaleOther90210Cancer Pain1361131No2020-07-14Oxycodone12.5 mcg/hourevery 12 hours122OpioidLowPost-operative pain managed with Hydrocodone.Pain ManagementFollow-upGeneral11:33:2506:17:3059
2997202987966256845MaleHispanic90210Post-Surgery Pain5341441No2023-08-02Codeine20 mgevery 4-6 hours111AnalgesicHighMeperidine prescribed for acute pain episodes.OrthopedicsConsultationSpecialized18:12:3603:07:3045
299892796301617974FemaleHispanic30301Fibromyalgia136920182No2020-12-19Codeine2.5 mgevery 4-6 hours124AnalgesicLowPrescribed Oxymorphone for severe pain.OrthopedicsRoutine Check-upGeneral13:50:4121:08:5131
2999395820149421061FemaleWhite10001Fibromyalgia133620124No2022-10-17Hydromorphone20 mgevery 4-6 hours31OpioidLowHistory of depression, taking Oxycodone for post-surgery pain.OncologyFollow-upSpecialized15:32:5405:52:2913